/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    EuclideanDistance.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.core;

import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.neighboursearch.PerformanceStats;

/**
 * <!-- globalinfo-start --> Implementing Euclidean distance (or similarity)
 * function.<br/>
 * <br/>
 * One object defines not one distance but the data model in which the distances
 * between objects of that data model can be computed.<br/>
 * <br/>
 * Attention: For efficiency reasons the use of consistency checks (like are the
 * data models of the two instances exactly the same), is low.<br/>
 * <br/>
 * For more information, see:<br/>
 * <br/>
 * Wikipedia. Euclidean distance. URL
 * http://en.wikipedia.org/wiki/Euclidean_distance.
 * <p/>
 * <!-- globalinfo-end -->
 *
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;misc{missing_id,
 *    author = {Wikipedia},
 *    title = {Euclidean distance},
 *    URL = {http://en.wikipedia.org/wiki/Euclidean_distance}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 *
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 *  -D
 *  Turns off the normalization of attribute 
 *  values in distance calculation.
 * </pre>
 * 
 * <pre>
 *  -R &lt;col1,col2-col4,...&gt;
 *  Specifies list of columns to used in the calculation of the 
 *  distance. 'first' and 'last' are valid indices.
 *  (default: first-last)
 * </pre>
 * 
 * <pre>
 *  -V
 *  Invert matching sense of column indices.
 * </pre>
 * 
 * <!-- options-end -->
 *
 * @author Gabi Schmidberger (gabi@cs.waikato.ac.nz)
 * @author Ashraf M. Kibriya (amk14@cs.waikato.ac.nz)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class EuclideanDistance extends NormalizableDistance implements Cloneable, TechnicalInformationHandler {

    /** for serialization. */
    private static final long serialVersionUID = 1068606253458807903L;

    /**
     * Constructs an Euclidean Distance object, Instances must be still set.
     */
    public EuclideanDistance() {
        super();
    }

    /**
     * Constructs an Euclidean Distance object and automatically initializes the
     * ranges.
     * 
     * @param data the instances the distance function should work on
     */
    public EuclideanDistance(Instances data) {
        super(data);
    }

    /**
     * Returns a string describing this object.
     * 
     * @return a description of the evaluator suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "Implementing Euclidean distance (or similarity) function.\n\n" + "One object defines not one distance but the data model in which " + "the distances between objects of that data model can be computed.\n\n" + "Attention: For efficiency reasons the use of consistency checks " + "(like are the data models of the two instances exactly the same), " + "is low.\n\n" + "For more information, see:\n\n" + getTechnicalInformation().toString();
    }

    /**
     * Returns an instance of a TechnicalInformation object, containing detailed
     * information about the technical background of this class, e.g., paper
     * reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
        TechnicalInformation result;

        result = new TechnicalInformation(Type.MISC);
        result.setValue(Field.AUTHOR, "Wikipedia");
        result.setValue(Field.TITLE, "Euclidean distance");
        result.setValue(Field.URL, "http://en.wikipedia.org/wiki/Euclidean_distance");

        return result;
    }

    /**
     * Calculates the distance between two instances.
     * 
     * @param first  the first instance
     * @param second the second instance
     * @return the distance between the two given instances
     */
    public double distance(Instance first, Instance second) {
        return Math.sqrt(distance(first, second, Double.POSITIVE_INFINITY));
    }

    /**
     * Calculates the distance (or similarity) between two instances. Need to pass
     * this returned distance later on to postprocess method to set it on correct
     * scale. <br/>
     * P.S.: Please don't mix the use of this function with distance(Instance first,
     * Instance second), as that already does post processing. Please consider
     * passing Double.POSITIVE_INFINITY as the cutOffValue to this function and then
     * later on do the post processing on all the distances.
     *
     * @param first  the first instance
     * @param second the second instance
     * @param stats  the structure for storing performance statistics.
     * @return the distance between the two given instances or
     *         Double.POSITIVE_INFINITY.
     */
    public double distance(Instance first, Instance second, PerformanceStats stats) { // debug method pls remove after use
        return Math.sqrt(distance(first, second, Double.POSITIVE_INFINITY, stats));
    }

    /**
     * Updates the current distance calculated so far with the new difference
     * between two attributes. The difference between the attributes was calculated
     * with the difference(int,double,double) method.
     * 
     * @param currDist the current distance calculated so far
     * @param diff     the difference between two new attributes
     * @return the update distance
     * @see #difference(int, double, double)
     */
    protected double updateDistance(double currDist, double diff) {
        double result;

        result = currDist;
        result += diff * diff;

        return result;
    }

    /**
     * Does post processing of the distances (if necessary) returned by
     * distance(distance(Instance first, Instance second, double cutOffValue). It is
     * necessary to do so to get the correct distances if distance(distance(Instance
     * first, Instance second, double cutOffValue) is used. This is because that
     * function actually returns the squared distance to avoid inaccuracies arising
     * from floating point comparison.
     * 
     * @param distances the distances to post-process
     */
    public void postProcessDistances(double distances[]) {
        for (int i = 0; i < distances.length; i++) {
            distances[i] = Math.sqrt(distances[i]);
        }
    }

    /**
     * Returns the squared difference of two values of an attribute.
     * 
     * @param index the attribute index
     * @param val1  the first value
     * @param val2  the second value
     * @return the squared difference
     */
    public double sqDifference(int index, double val1, double val2) {
        double val = difference(index, val1, val2);
        return val * val;
    }

    /**
     * Returns value in the middle of the two parameter values.
     * 
     * @param ranges the ranges to this dimension
     * @return the middle value
     */
    public double getMiddle(double[] ranges) {

        double middle = ranges[R_MIN] + ranges[R_WIDTH] * 0.5;
        return middle;
    }

    /**
     * Returns the index of the closest point to the current instance. Index is
     * index in Instances object that is the second parameter.
     *
     * @param instance  the instance to assign a cluster to
     * @param allPoints all points
     * @param pointList the list of points
     * @return the index of the closest point
     * @throws Exception if something goes wrong
     */
    public int closestPoint(Instance instance, Instances allPoints, int[] pointList) throws Exception {
        double minDist = Integer.MAX_VALUE;
        int bestPoint = 0;
        for (int i = 0; i < pointList.length; i++) {
            double dist = distance(instance, allPoints.instance(pointList[i]), Double.POSITIVE_INFINITY);
            if (dist < minDist) {
                minDist = dist;
                bestPoint = i;
            }
        }
        return pointList[bestPoint];
    }

    /**
     * Returns true if the value of the given dimension is smaller or equal the
     * value to be compared with.
     * 
     * @param instance the instance where the value should be taken of
     * @param dim      the dimension of the value
     * @param value    the value to compare with
     * @return true if value of instance is smaller or equal value
     */
    public boolean valueIsSmallerEqual(Instance instance, int dim, double value) { // This stays
        return instance.value(dim) <= value;
    }

}
